Analysis of various physiological signals to study human mental state is an emerging and widely recognized field. Cognitive load is a total amount of mental activity imposed on a memory of a subject while performing any cognitive task. Real time monitoring of the cognitive load is required in a variety of fields ranging from personalized learning to air-traffic monitoring. Mental state of the subject changes due to the cognitive load imparted on his/her brain and performance of the subject may also drastically change based on a level of the cognitive load. Physiological measurements of the subject may give more unbiased, reliable and accurate metrics than performance based indices.
Hence, analysis of brain signals is gaining increased importance. Several techniques are available to analyze the brain signals namely, Electroencephalography (EEG), functional Magnetic Resonance Imaging (fMRI), functional Near Infrared Spectroscopy (fNIRS), and the like. Neuro-physiological changes in the brain to a given stimulus can be used to differentiate between human thinking processes for different levels of cognitive tasks. As compared to other available techniques, the EEG technique is relatively inexpensive, non-invasive and has excellent temporal resolution.
There are various EEG devices available in the market to measure the cognitive load of the subject. One type of the EEG devices may be high resolution EEG device which may fall under precise medical diagnostic devices and other type may be low resolution EEG devices used for Brain-Computer Interfacing (BCI) applications. The low resolution EEG devices come with lower number of EEG channels; hence often miss sensitive positions of the EEG channels related to the cognitive load. Further, the sensitive positions of the EEG channels are subjective. The sensitive positions of the EEG channels may vary from person to person or based on stimulus types.
The low resolution EEG devices come with lower number of EEG channels. Hence while determining the cognitive load of the subject, the low resolution EEG devices pose major challenges in EEG signal processing and feature extraction. While applying standard pre-processing techniques like Independent Component Analysis (ICA), Common Spatial Pattern Filtering, due to lower number of EEG channels required processing accuracy cannot be achieved. Further, complexity level in processing of the EEG signals is quite high, hence if one needs to use reduced number of EEG channels, then it is important to know valid positions of the EEG channels on the skull that give best results. Analysis for the valid positions of EEG channels can be done as a subject dependent or as a global finding (subject independent). Therefore finding the valid positions of the EEG channels plays a crucial role while determining the cognitive load by using the low resolution EEG devices. Finding the valid positions of the EEG channels is also essential for addressing variability of the subjects, variability of the stimulus and also for artifact removal from the EEG signals.